Applications can include many features, which users can select from to perform tasks. As an example, in Adobe® Photoshop®, when creating a composition, a user could select from cut, paste, select, mask, resize, color select, magic wand, crop, clone stamp, healing brush, and other features. Accessing features, however, may be difficult for users. For example, the number and functionality of features of an application may change over time, the application interface may change, some features may be difficult to discover in a user interface, and some features may be complex, especially for new users. Furthermore, a user may have an objective which cannot be met by a single feature. In such a case, the user may not know how to combine features to achieve that objective or the most efficient way to achieve that objective, resulting in computationally intensive tasks. These and other issues can create barriers between the interactions between computing systems and users.
Accordingly, feature-related recommendations may be provided to users in the form of tutorials and notifications to mitigate some of these issues. Using conventional approaches, however, these recommendations are unlikely to be relevant to many users, which may be at different experience levels, or desire to accomplish different objectives. Furthermore, recommendations in conventional approaches are limited to an application being used, whereas a different application may be more relevant to a user. For instance, in some conventional approaches, the most popular features of an application are recommended to users. In addition to irrelevant recommendations, new features are unlikely to become popular and recommended. In another approach, users are assigned to segments based on how often they use an application (e.g., to capture experience levels), and a particular user is presented with recommendations manually curated for that user's segment. This approach fails to account for different user objectives and other variations between users, which limits the relevance of recommendations.